966 research outputs found
Performance Modelling and Optimisation of Multi-hop Networks
A major challenge in the design of large-scale networks is to predict and optimise the
total time and energy consumption required to deliver a packet from a source node to a
destination node. Examples of such complex networks include wireless ad hoc and sensor
networks which need to deal with the effects of node mobility, routing inaccuracies, higher
packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the
computational limitations of the nodes. They also include more reliable communication
environments, such as wired networks, that are susceptible to random failures, security
threats and malicious behaviours which compromise their quality of service (QoS) guarantees.
In such networks, packets traverse a number of hops that cannot be determined
in advance and encounter non-homogeneous network conditions that have been largely
ignored in the literature. This thesis examines analytical properties of packet travel in
large networks and investigates the implications of some packet coding techniques on both
QoS and resource utilisation.
Specifically, we use a mixed jump and diffusion model to represent packet traversal
through large networks. The model accounts for network non-homogeneity regarding
routing and the loss rate that a packet experiences as it passes successive segments of a
source to destination route. A mixed analytical-numerical method is developed to compute
the average packet travel time and the energy it consumes. The model is able to capture
the effects of increased loss rate in areas remote from the source and destination, variable
rate of advancement towards destination over the route, as well as of defending against
malicious packets within a certain distance from the destination. We then consider sending
multiple coded packets that follow independent paths to the destination node so as to
mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium
and obtain the time-dependent properties of the packet’s travel process, allowing us to
compare the merits and limitations of coding, both in terms of delivery times and energy
efficiency. Finally, we propose models that can assist in the analysis and optimisation
of the performance of inter-flow network coding (NC). We analyse two queueing models
for a router that carries out NC, in addition to its standard packet routing function. The
approach is extended to the study of multiple hops, which leads to an optimisation problem
that characterises the optimal time that packets should be held back in a router, waiting
for coding opportunities to arise, so that the total packet end-to-end delay is minimised
Content Distribution by Multiple Multicast Trees and Intersession Cooperation: Optimal Algorithms and Approximations
In traditional massive content distribution with multiple sessions, the
sessions form separate overlay networks and operate independently, where some
sessions may suffer from insufficient resources even though other sessions have
excessive resources. To cope with this problem, we consider the universal
swarming approach, which allows multiple sessions to cooperate with each other.
We formulate the problem of finding the optimal resource allocation to maximize
the sum of the session utilities and present a subgradient algorithm which
converges to the optimal solution in the time-average sense. The solution
involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope
with this difficulty by using a column generation method, which reduces the
number of Steiner-tree computations. Furthermore, we allow the use of
approximate solutions to the Steiner-tree subproblem. We show that the
approximation ratio to the overall problem turns out to be no less than the
reciprocal of the approximation ratio to the Steiner-tree subproblem.
Simulation results demonstrate that universal swarming improves the performance
of resource-poor sessions with negligible impact to resource-rich sessions. The
proposed approach and algorithm are expected to be useful for
infrastructure-based content distribution networks with long-lasting sessions
and relatively stable network environment
Queue stability analysis in network coded wireless multicast.
In this dissertation queue stability in wireless multicast networks with packet erasure channels is studied. Our focus is on optimizing packet scheduling so as to maximize throughput. Specifically, new queuing strategies consisting of several sub-queues are introduced, where all newly arrived packets are first stored in the main sub-queue on a first-come-first-served basis. Using the receiver feedback, the transmitter combines packets from different sub-queues for transmission. Our objective is to maximize the input rate under the queue stability constraints. Two packet scheduling and encoding algorithms have been developed. First, the optimization problem is formulated as a linear programming (LP) problem, according to which a network coding based optimal packet scheduling scheme is obtained. Second, the Lyapunov optimization model is adopted and decision variables are defined to derive a network coding based packet scheduling algorithm, which has significantly less complexity and smaller queue backlog compared with the LP solution. Further, an extension of the proposed algorithm is derived to meet the requirements of time-critical data transmission, where each packet expires after a predefined deadline and then dropped from the system. To minimize the average transmission power, we further derive a scheduling policy that simultaneously minimizes both power and queue size, where the transmitter may choose to be idle to save energy consumption. Moreover, a redundancy in the schedules is inadvertently revealed by the algorithm. By detecting and removing the redundancy we further reduce the system complexity. Finally, the simulation results verify the effectiveness of our proposed algorithms over existing works
A Survey of QoS Routing Protocols for Ad Hoc Networks
The aim of this paper is to give a big survey in enhancing the balance of the routing load and the consumption of resources using network layer metrics for the path discovery in the MAODV protocol. A ad hoc network (AD HOC NETWORKS) consists of a collection of wireless mobile nodes, which form a temporary network without relying on any existing infrastructure or centralized administration. The bandwidth of the ad hoc networks architecture is limited and shared between the participating nodes in the network, therefore an efficient utilization of the network bandwidth is very important. Multicasting technology can minimize the consumption of the link bandwidth and reduce the communication cost too. As multimedia and group-oriented computing gains more popularity for users of ad hoc networks, the effective Quality of Service (QoS) of the multicasting protocol plays a significant role in ad hoc networks. In this paper we propose a reconstruction of the MAODV protocol by extending some featuring QoS in MAODV. All simulations are prepared with the NS2 simulator and compare the performance of this algorithm with the MAODV algorithm. The achieved results illustrate faster path discovery and more performing routing balance in the use of MAODV-Extension.This paper would give relatively a modest support in Mobile Technology according to QoS communication
Dynamic Rate Adaptation for Improved Throughput and Delay in Wireless Network Coded Broadcast
In this paper we provide theoretical and simulation-based study of the
delivery delay performance of a number of existing throughput optimal coding
schemes and use the results to design a new dynamic rate adaptation scheme that
achieves improved overall throughput-delay performance.
Under a baseline rate control scheme, the receivers' delay performance is
examined. Based on their Markov states, the knowledge difference between the
sender and receiver, three distinct methods for packet delivery are identified:
zero state, leader state and coefficient-based delivery. We provide analyses of
each of these and show that, in many cases, zero state delivery alone presents
a tractable approximation of the expected packet delivery behaviour.
Interestingly, while coefficient-based delivery has so far been treated as a
secondary effect in the literature, we find that the choice of coefficients is
extremely important in determining the delay, and a well chosen encoding scheme
can, in fact, contribute a significant improvement to the delivery delay.
Based on our delivery delay model, we develop a dynamic rate adaptation
scheme which uses performance prediction models to determine the sender
transmission rate. Surprisingly, taking this approach leads us to the simple
conclusion that the sender should regulate its addition rate based on the total
number of undelivered packets stored at the receivers. We show that despite its
simplicity, our proposed dynamic rate adaptation scheme results in noticeably
improved throughput-delay performance over existing schemes in the literature.Comment: 14 pages, 15 figure
Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning
Multicasting in wireless systems is a natural way to exploit the redundancy
in user requests in a Content Centric Network. Power control and optimal
scheduling can significantly improve the wireless multicast network's
performance under fading. However, the model based approaches for power control
and scheduling studied earlier are not scalable to large state space or
changing system dynamics. In this paper, we use deep reinforcement learning
where we use function approximation of the Q-function via a deep neural network
to obtain a power control policy that matches the optimal policy for a small
network. We show that power control policy can be learnt for reasonably large
systems via this approach. Further we use multi-timescale stochastic
optimization to maintain the average power constraint. We demonstrate that a
slight modification of the learning algorithm allows tracking of time varying
system statistics. Finally, we extend the multi-timescale approach to
simultaneously learn the optimal queueing strategy along with power control. We
demonstrate scalability, tracking and cross layer optimization capabilities of
our algorithms via simulations. The proposed multi-timescale approach can be
used in general large state space dynamical systems with multiple objectives
and constraints, and may be of independent interest.Comment: arXiv admin note: substantial text overlap with arXiv:1910.0530
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